Towards Safer Transportation: a self-supervised learning approach for traffic video deraining

TL;DR

This paper introduces a self-supervised learning method to effectively remove rain streaks from traffic videos, improving visual quality and reliability of traffic monitoring during adverse weather conditions.

Contribution

It proposes a novel two-stage self-supervised approach specifically designed for rain removal in traffic videos, addressing both intra- and inter-frame noise.

Findings

Model achieves satisfactory visual quality improvements.

Peak Signal-Noise Ratio is significantly enhanced.

Effective in challenging rainy weather conditions.

Abstract

Video monitoring of traffic is useful for traffic management and control, traffic counting, and traffic law enforcement. However, traffic monitoring during inclement weather such as rain is a challenging task because video quality is corrupted by streaks of falling rain on the video image, and this hinders reliable characterization not only of the road environment but also of road-user behavior during such adverse weather events. This study proposes a two-stage self-supervised learning method to remove rain streaks in traffic videos. The first and second stages address intra- and inter-frame noise, respectively. The results indicated that the model exhibits satisfactory performance in terms of the image visual quality and the Peak Signal-Noise Ratio value.